Cyber security is among the most complex and rapidly evolving issues and has been the focus of present day organizations. Cyber security risk management is the process of managing or reducing potentially harmful and uncertain events that posse as threats to cyber security. It involves looking at what could go wrong on the cyber space and deciding on ways to prevent or minimize their occurrences or effects. One of the prominent cyber security risk management techniques is the Game Theoretic Approach (GTA), which focuses on the use of resources, internal controls, information sharing, technical improvements, behavioral or organizational scale-ups and cyber insurance for cyber risk management. This paper presents a review of game theoretic-based model for cyber security risk management. Specifically, issues on modeling, some related works and significance of game theoretic approach to cyber security risk management are presented. Findings from the review revealed the peculiarities and specificity of each model. It is also revealed that the models are just evolving and require much improvement.

The use of fingerprint for human identity management has been on the rise lately. Reasons adduced for this include its high level of uniqueness, availability, consistency and universality. The task of human identity management based on fingerprint technology involves a number of processes which include enrolment, enhancement, feature and singular points detection and extraction and pattern matching. Detection and extraction of genuine and reliable feature and singular points are paramount for reliable pattern matching. The limitations of some existing fingerprint singular point detection algorithms include inaccurate detection and failure with some fingerprint pattern and poor quality images. In this paper, a modified Poincare Index fingerprint singular point detection algorithm is proposed for resolving these limitations. The results of the experimental study of the new algorithm on Dataset DB1 of FVC2000 standard fingerprint database show that the new algorithm reliably and adequately detected singular points from fingerprints of all patterns and qualities.

Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS) have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement.

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